As shown in Fig. REF , given an interacting two-hand image, we first obtain the modal and amodal masks of both hands using the Hand Amodal Segmentation Module (HASM). We simply adapt the off-the-shelf instance segmentation model, i.e. SegFormer [1]}, to fit in our two-hand amodal segmentation tasks. Specifically, we increase the number of decode heads from one to four to predict four kinds of segmentation masks, namely the right hand amodal mask \(M_{ra}\) , the right hand visible mask \(M_{rv}\) , the left hand amodal mask \(M_{la}\)  and the left hand visible mask \(M_{lv}\) . These segmentation masks contain (1) spatial localization information to roughly localize the left/right hand, and (2) rich cues about the occluded regions for de-occlusion and the distractor regions for removal.
